OVERVIEW upon consumers require- ments. Particle swarm optimization (PSO)


Cloud  computing is
a model  for enabling ubiquitous, convenient, on-demand net-
work  access
to a shared pool of configurable computing resources that can be rapidly
provisioned and  released with  service  provider interaction .It is a new
 paradigm for
delivering on-demand resources for customers through internet.
A service is a mech- anism
 is capable of providing one  or more
which  it is possible
to use in compliance with
 provider defined
restrictions and  rules  and  through an in- terface
 .There  are three  services  models in cloud. 
are Software  as a service:
 A software or application that is executing on a vendors infrastructure is
recognized as a service provided
that the consumer has limited permission to access
and the provision
is through a thin client
or a program interface for sending data  and
receiving results.

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  The consumer is
unaware of
the application providers
infrastructure and
 has lim- ited authority to configure some settings. Platform
as a service:  In this services
model, the service
vendor provides moderate
basic requisites, including
the operating system, network and
 servers, and
 development tools
 to allow  the 
consumer to develop ac-
quired applications or software and manage their configurable settings. Infrastructure
as a service: The cloud service consumer has developed the required applications and needs  only a basic infrastructure. In
such cases, processors,
networks, and storage can
be provided by
vendors as services with consumer provisions.
Cloud  service Ranking is needed to
cloud service consumers to choose appropriate cloud service from a pool of available cloud  services.   The Qos parameters such 
as response time,  availability, throughput,
etc.  are used  to rank
 the cloud  services  based  upon consumers require-
ments.  Particle  swarm optimization (PSO) is a computational method that
 optimizes a
problem by iteratively trying  to improve a candidate solution
with regard to a given measure of quality. PSO optimizes a
problem by having a population of candidate so- lutions, here dubbed particles, and moving these particles around in the search-space according to simple  mathematical formulae.






            In cloud
service ranking approach only few quantifiable parameters QoS attributes were
used for ranking. Several non-quantifiable QoS attributes have major impact in
the ranking and selection process. Also, static ranking of cloud services may
provide inappropriate cloud service to cloud service consumers as the
requirements of one consumer vary with another. The dynamic ranking and
selection of cloud services is solved by designing a cloud broker model with
several components work together to perform Cloud  Service Ranking and Selection using  Particle Swarm  Optimization.



The accuracy achieved through this
project is 94% which can be increased further.

The classifiers considered can be
changed further to improve efficiency.

The proposed project is subject to text
mining and so still other mining techniques like spatial and correlation
techniques can be used.











2.1 REVIEW     

In this paper, they survey state-of-the-art Cloud  services  selection  approaches, which are analyzed from the following five perspectives: decision-making techniques; data representation models;   parameters and
 characteristics of Cloud  services;  contexts, purposes.  After
 comparing and
 summarizing the  approaches they
 identify the  pri- mary
 research issues  in Cloud  service 
selection.   Optimization-based
approaches for Cloud  service selection:  Chang  etal.(2012) developed a
dynamic programming-based
algorithm to select Cloud  storage providers that can maximize the data survival prob- ability
or the amount of surviving data, subject
to a fixed budget. They formulated the problem of multiple storage
service  provider selection
 into a probability model  with clearly defined object functions and cost measurements. The availability of the storage service is quantitatively analyzed by
two methods minimum failure probability with a given budget, and maximum validity
with a given budget. Sundareswaran
etal.(2012) employed a greedy algorithm- based  method for Cloud  service
 selection.   They pro- posed the use of the B+ tree to index
 providers (i.e.CSPindexing) and encode  services
 The indexing structure supports the  indexing of service
properties and
the modeling of their relative  importance, as ordered by users.  It enables  fast
information retrieval for decision  makers. Martens
and Teuteberg
(2012) developed a scalable 
mathematical decision  model
 for discrete dynamic opti- mization problems in Cloud  service selection.  The model
 helps organizations to iden- tify
suitable Cloud  services by minimizing costs and risks. An AHP-based approach is
proposed to
measure the relative  importance of the services
 in a business process  and the  relative
 importance of security parameters in a risk evaluation process. 
 Finally, decisions can be made  by solving  the  formulated mathematical models.  Optimiza- tion techniques, such as linear,  non-
linear,  and genetic  algorithms, are recommended as the  tools  for solving
 models, depending on 
the  specific  service  outsourcing
narios.   Identified issues:  The open  issues  on contemporary Cloud  service
 selection approaches are

 1.  Lack of a marketplace
for Cloud  service
 publication and  transac- tion:  Cloud  services  do not have  a standard for service  publication
and  registry. The
lack of detailed service  QoS information makes  it difficult  for service  users  to make educated purchasing
decisions. Cloud  service
 to rate 
and  make comments on services,  but there  is no feedback from users.

 2. Lack of normalization
for Cloud service
description serving different kinds of users:  The flourishing of Cloud services highlights the need for a unified specification for Cloud  services.  A high level of abstraction and  support for
the simple  publication, discovery, selection,
 and use of resources for both 
service  providers and
 is needed.

3. Lack of a search
 engine system for the 
automatic identification and
 updating of Cloud  service  information: Cloud  service  specification lacks a standard form,  especially for IaaS and  PaaS. The service  information is typically published as plain  text on a Web page,  which  usually
narrows to a functional description rather than being complete enough to include tech- nical details.
 Such incompleteness prevents keyword-based search  engines returning accurate services.

4. Lack of an efficient
means to deal with qualitative parameters and fuzzy expression:
Qualitative non-functional properties such as security and availabil-
ity increase  the fuzziness of service  evaluation. Current techniques are more
 focused on 
quantitative criteria  that  can  be measured via 
precise  numerical values
 as response time, storage space and  network latency.  Hence  an efficient
method of han- dling  uncertainty and  fuzziness in service  specification and  user  requirements needs to be taken  into  account for
the  chosen  services. 

5.  Less
concern  on multi-tenancy service  selection.   

6.  Lack
of an advanced multi-criteria-based measurement of
user preferences.

7. Lack of consideration of the interdependency of

8.Lack of long term performance predication and dynamic application strategy.















The data mining technique that is being
used comprise of a model that helps in training the train data set. The model
is made up of techniques without any Cross Validations and repeats. Hence the
obtained accuracy is around 92%. The false positive rate is also high. Though
all kind of vulnerabilities are considered, the results of all vulnerabilities
are of the same accuracy. The vulnerabilities include XSS, SQL Injection.    


all web applications is moving from a traditional deployment strategy to an
on-demand cloud environment. It is highly difficult for the cloud service
consumers to  choose wisely between the
available cloud providers. On the other hand, each and every cloud provider may
have interest on different parameters to be set for their infrastructures.
Also, there is no common registry to register the service level agreement of
cloud service as that of the web services. Hence, it becomes difficult for the
consumers to choose appropriately the required services and thereby cloud
service providers.






The proposed cloud broker
architecture has three  components. They
are Cloud  Service Consumer; the
individual or an organization that requires a cloud service either to deploy an
application or for application development, Cloud  Broker; is the middleware that receives input
from the cloud consumers as well as the cloud service providers. It checks the
service level objectives with that of the service level agreement and makes the
decision processing to rank and thereby select the cloud service. Cloud  Service Provider; is an entity that provides
cloud services to the end users or cloud service consumers.





         The Cloud
Broker has two databases SLA repository and
Qos information reposi- tory and has probation manager, rank manager, co-ordination
Agent and search agent. The SLAs of cloud service providers
are stored in the SLA Repository of cloud broker. The SLA document consists  of the quantifiable and non- quantifiable Qos parameters
which  include service  name,  cloud
 provider, security, availability, processor speed,
cost per hour,
 storage, bandwidth, performance, etc.,

             The Probation Manager :  takes SLA from SLA repository and  checks the parameters of SLA during the probation period. After the validation it informs the rank  manager
with updated parameters.

             The Rank Manager: has
rank  table 
and  updates the
rank  table 
with  SLA parameter given by probation manager. Rank table contains ranking of
cloud  services  according to the SLA parameters. If a service  is longer
 by a consumer, then  rank  manager gives the service to probation
manager for validation.

The Qos
information repository: feedback of the past customer experience
are stored
in Qos Information Repository.



1. Build SLA
and Design Cloud Broker

– Probation Manager

– Rank Manager

2. Build Qos Information
Repository & add into Cloud  Broker

– Co-ordination Agent

– Search Agent

Integration of cloud service consumers requirements with broker

Cloud  Service Ranking and Selection using  PSO







SLA Repository

         The SLA from  cloud  service  providers for
differenet cloud
 was  col-lected.
 The SLA document consists
of the quantifiable Qos parametersuca  as service




Figure 2: Input SLA



cloud provider, security, availability, processor
speed, cost per hour, storage and non Qos
parameter as
bandwidth etc. The SLA parameters are collected and  stored in Mysql server.

















Design of Cloud  Broker

The cloud broker
has four entities Probation Manager,
Rank Manager ,Co-ordination Agent,
Search Agent. Using cloudsim, the broker  is created with  entities  along
 with cloudlets.




























Probation  Manager

Step1:Simulation of Probation Manager



Figure 5: Probation
Manager simulation



Step2:       The
Probation Manager
gets  the  SLA
parameters from  the  database and
populate the table with SLA.



Figure 6: Probation
Manager gets SLA’s








4.3.4  Timeline






























      1 GB and above


      Dual core and above

     Hard Disk

      80 GB and above

hardware requirements






Operating System

Windows 7

Application Environment


Programming Language



software requirements






data thus has been filtered to figure out what are the data that are vulnerable
and non-vulnerable data. The improved accuracy helps in better filtering of
data. The future work is to implement Ensembling models in order to achieve
still better accuracy results. Also the method of preventing the vulnerable
data can also be proposed thereby preventing the impact of vulnerable data
during the transmission of it and safeguarding the entire system.

is a general term for combining many classifiers by averaging or voting. It is
a form of meta learning in that it focuses on how to merge results of arbitrary
underlying classifiers. Generally, ensembles of classifiers perform better than
single classifiers, and the averaging process allows for more granularity of
choice in the bias-variance tradeoff.

of ensemble techniques include bagging, boosting, model
averaging, and weak learner theory.

An obvious strategy is
thus to implement as many different solvers as possible and ensemble them all
together, a sort of “More Models are Better” approach.

Text Mining is the key
to determine the vulnerable data at the source and efficient methods in
adopting text mining will improve the mining results.




















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